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Improved RANSAC Point Cloud Spherical Target Detection and Parameter Estimation Method Based on Principal Curvature

Qinghua Wu1, Jiacheng Liu1, Can Gao1

  • 1Hubei Key Laboratory of Modern Manufacturing Quantity Engineering, School of Mechanical Engineering, Hubei University of Technology, Wuhan 430068, China.

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Summary

This study introduces an improved random sample consensus (RANSAC) algorithm for faster and more accurate spherical object detection and parameter estimation in point cloud data. The novel method enhances coordinate unification for large-scale measurements.

Keywords:
3D point cloudRANSAClarge-scale combined measurementsphere parameter estimationspherical target detection

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Area of Science:

  • Geomatics and Measurement Science
  • Computer Vision and Pattern Recognition

Background:

  • Spherical targets are crucial for unifying coordinate systems in large-scale measurements using scanned point cloud data.
  • Existing point cloud sphere detection methods suffer from inaccuracies and slow processing speeds.

Purpose of the Study:

  • To develop a novel, efficient, and accurate method for spherical object detection and parameter estimation.
  • To address the limitations of conventional algorithms in point cloud data processing.

Main Methods:

  • An improved random sample consensus (RANSAC) algorithm is proposed, integrating principal curvature calculation and k-d nearest neighbor search for enhanced sampling.
  • The RANSAC method is combined with the total least squares method for precise parameter estimation of detected spheres.

Main Results:

  • The enhanced RANSAC algorithm significantly improves the quality of selected sample points, leading to increased detection speed.
  • Experimental results show superior accuracy and speed in sphere parameter estimation compared to the conventional RANSAC algorithm.

Conclusions:

  • The proposed method offers a more robust and efficient solution for spherical object detection in point cloud data.
  • This advancement facilitates more accurate coordinate unification in large-scale metrology applications.